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首页> 外文期刊>Journal of Hydroinformatics >Multi-objective calibration of the physically based, spatially distributed SHETRAN hydrological model
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Multi-objective calibration of the physically based, spatially distributed SHETRAN hydrological model

机译：基于物理的，空间分布的SHETRAN水文模型的多目标校准

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Physically based, spatially distributed hydrological models have mostly been calibrated manually; a few were calibrated automatically but without full consideration of conflicting multi-objectives. Here, we successfully applied the non-dominated sorting genetic algorithm II (NSGA-II) and its two variants, namely the reference point-based R-NSGA-II and the extension ER-NSGA-II, to multi-objective, automatic calibration of the SHETRAN hydrological model. Moreover, we demonstrated the possibility of speeding up the calibration process by adjusting the recombination and mutation parameters of the optimization algorithms. The simulated binary crossover and polynomial mutation were used with respective probabilities of 0.9 and 0.1, and crossover and mutation distribution indices (eta(c), eta(m)) with values of (0.5, 0.5), (2.0, 0.5) and (20., 20.). The results indicate that the use of smaller (eta(c), eta(m)) speeded up the optimization process of SHETRAN calibration, especially during the initial stage, for all three algorithms; however, the use of the R-NSGA-II and ER-NSGA-II did not provide a more efficient optimization compared to the NSGA-II. The broad search of the algorithms, enabled by the generation of diversified solutions due to the use of small (eta(c), eta(m)), contributed to the improved efficiency. Finally, we successfully validated the optimal solutions for both the basin outlet and the internal gauging stations.
机译：基于物理的，空间分布的水文模型大多是手动校准的。其中一些是自动校准的，但没有充分考虑冲突的多目标。在这里，我们成功地将非支配排序遗传算法II（NSGA-II）及其两个变体（基于参考点的R-NSGA-II和扩展ER-NSGA-II）应用于多目标自动校准SHETRAN水文模型的模型。此外，我们展示了通过调整优化算法的重组和突变参数来加快校准过程的可能性。使用模拟的二元交叉和多项式突变，各自的概率分别为0.9和0.1，交叉和突变分布指数（eta（c），eta（m））的值分别为（0.5，0.5），（2.0，0.5）和（ 20.，20.）。结果表明，对于所有三种算法，使用较小的（eta（c），eta（m））可以加快SHETRAN校准的优化过程，尤其是在初始阶段。但是，与NSGA-II相比，R-NSGA-II和ER-NSGA-II的使用没有提供更有效的优化。由于使用了小数（eta（c），eta（m）），因此可以生成多种解决方案，从而对算法进行了广泛的搜索，从而提高了效率。最后，我们成功地验证了盆地出口和内部计量站的最佳解决方案。

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